DevelopmentHell.R
In TBooker/PicMin: PicMin
rm(list=ls())
colorBlindBlack8 <- c("#999999", "#E69F00", "#56B4E9", "#009E73",
"#F0E442", "#0072B2", "#D55E00", "#CC79A7")
library(rSymPy)
library(poolr)
library(reshape2)
library(ggplot2)
library(foreach)
library(doParallel)
library(ggpubr)
#' @title Beta probability
#' @param p p-value
#' @param i rank
#' @param n The number of inputs
#' @importFrom "stats" "pbeta" "qbeta"
F_i = function(p, i, n)
{
a = i
b = n-i+1
res = pbeta(q = p, shape1 = a, shape2 = b, lower.tail = T)
return(res)
}
#' @title ordmeta
#' @description Minimum Marginal P-value in joint order distribution
#' @param p A vector of p-values
#' @param is.onetail Logical. If set TRUE, p-values are combined without considering the direction of effect, and vice versa. Default: TRUE.
#' @param eff.sign A vector of signs of effect sizes. It works when is.onetail = FALSE
#' @importFrom "rSymPy" "Var" "sympy"
#' @return p : Combined p-value
#' @return optimal_rank : Optimal rank where minimum marginal p-value exists.
#' @return eff.p.idx : Index of effective p-values
#' @return MMP : Minimum marginal p-value
#' @return overall.eff.direction : The direction of combined effects.
#' @examples \donttest{ordmeta(p=c(0.01, 0.02, 0.8, 0.25), is.onetail=FALSE, eff.sign = c(1,1,1,-1))}
#' @export
ordmeta = function(p, is.onetail = TRUE, eff.sign=NULL)
{
direc = eff.sign
if(is.null(p)){stop("Input p-values are required.")}
if(!is.onetail & is.null(eff.sign)){stop("Input the direction of effects.")}
idx_na = which(is.na(p))
if(length(idx_na)>0){p = p[-idx_na]; eff.sign = eff.sign[-idx_na]}
ordmeta = function(p2)
{
ord = order(p2, decreasing = F)
pord = sort(p2, decreasing = F)
# get alpha = MIN(F_(i)(x)) {i={1..n}}
N = length(p2)
alpha = 1.01 # an arbitrary number larger than 1
for(i in 1:N)
{
alpha_temp = F_i(pord[i], i, N)
if(alpha_temp < alpha){idx_minimum = i; alpha = alpha_temp}
}
# symbolic integral
for(i in 1:N)
{
x = Var("x")
y = Var("y")
if(i==1)
{
templete = paste(i,"*integrate(1, (x, lob, y))")
lob = qbeta(p = alpha,shape1 = i, shape2 = N-i+1, lower.tail = T)
templete = gsub("lob", lob, templete)
}else if(i>1 & i<N){
integ = gsub(pattern = "y", replacement = "x", x = integ)
templete = paste(i, "*integrate(",integ,", (x, lob, y))")
lob = qbeta(p = alpha,shape1 = i, shape2 = N-i+1, lower.tail = T)
templete = gsub("lob", lob, templete)
}else if(i==N)
{
integ = gsub(pattern = "y", replacement = "x", x=integ)
templete = paste(i, "*integrate(",integ,", (x, lob, 1))")
lob = qbeta(p = alpha,shape1 = i, shape2 = N-i+1, lower.tail = T)
templete = gsub("lob", lob, templete)
}
#print(templete)
integ = sympy(templete)
}
res = 1-as.numeric(integ)
return(list(p=res, optimal_rank = idx_minimum, eff.p.idx = ord[1:idx_minimum], MMP = alpha))
}
if(is.onetail)
{
RES = ordmeta(p2 = p)
return(RES)
}else{
p1 = p2 = p
idx_pos = which(eff.sign >= 0)
idx_neg = which(eff.sign < 0)
p1[idx_pos] = p[idx_pos]/2
p1[idx_neg] = 1-p[idx_neg]/2
p2[idx_pos] = 1-p[idx_pos]/2
p2[idx_neg] = p[idx_neg]/2
RES1 = ordmeta(p2 = p1)
RES2 = ordmeta(p2 = p2)
if(RES1$p<=RES2$p){
RES = RES1; RES$overall.eff.direction = "+"
}else{
RES = RES2; RES$overall.eff.direction = "-"
}
RES$p = RES$p * 2
if(RES$p > 1.0){RES$p = 1.0}
return(RES)
}
}
p_value_adjustment <- function(p_vec){
p_adj = 1 - (1 - min(p_vec))^length(p_vec)
return(p_adj)
}
scale_p_values <- function(p_list, screen = 0.05){
# sort the list of $p$-values
p_sort_unscaled=sort(p_list)[2:length(p_list)]
p_sort <- (p_sort_unscaled - screen)/(1-screen)
return(p_sort)
}
order_stats_p_values_scaled <- function(p_list){
# sort the list of $p$-values
p_sort_unscaled <- sort(p_list)[2:length(p_list)]
p_sort <- (p_sort_unscaled - min(p_list))/(1-min(p_list))
#p_sort <- (p_sort_unscaled - min(p_list))/(1-min(p_list))
n = length(p_sort)
out_p = c()
for (j in 1:length(p_sort)){
# Grab the relevant $p$-value from the list
p_j=p_sort[j]
# Calculate the $p$-values of each of the order statistics (excluding the first gene)
out_p = append(out_p, pbeta(p_j,j,n+1-j) )
}
return(out_p)
}
order_stats_p_values_unscaled <- function(p_list){
# sort the list of $p$-values
p_sort <- sort(p_list)[2:length(p_list)]
#p_sort <- (p_sort_unscaled - min(p_list))/(1-min(p_list))
#p_sort <- (p_sort_unscaled - min(p_list))/(1-min(p_list))
n = length(p_sort)
out_p = c()
for (j in 1:length(p_sort)){
# Grab the relevant $p$-value from the list
p_j=p_sort[j]
# Calculate the $p$-values of each of the order statistics (excluding the first gene)
out_p = append(out_p, pbeta(p_j,j,n+1-j) )
}
return(out_p)
}
x<-function(){
## Let's try a Bayesian approach
# Bayes theorem:
# P(A|B) = P(B|A)P(A)/P(B)
# In our case, P(A|B) is what is the probability of the first order statistic given that
# we've screened on alpha_adap
#
# P(A) - the probability of the kth order stat
# P(B) - the probability of a gene being screened using alpha_adap
# P(B|A) - the probability that a gene was screened given that it was the kth order stat
# Here's a test case:
p_values <- c(0.001, 0.1, 0.12, 0.3, 0.4, 0.5, 0.8)
scaled_p_values <- scale_p_values(p_values)
# For the first order stat
alpha_adap <- 0.01
p_a <- pbeta(p_values[2],1,6+1-1)
p_b <- 1-alpha_adap
p_ba <- 1-pbeta(alpha_adap,1,6+1-1)
p_ab <- (p_ba*p_a)/p_b
pbeta(0.1,1,6+1-1)
}
generateNullData <- function(adaptation_screen, a, b, n){
temp <- c( rbeta(1,a,b),replicate(n-1, sample(10000,1)/10000) )
while (sum(temp<adaptation_screen)==0){
temp <- c( rbeta(1,a,b),replicate(n-1, sample(10000,1)/10000) )
}
return(temp)
}
generateAltData <- function(adaptation_screen, max, k, n){
if (n-k ==0){
temp <- c(replicate(k, sample(max,1)/10000))
}
else if(k==0){
temp<- c(replicate(n, sample(10000,1)/10000) )
}
else{
temp <- c(replicate(k, sample(max,1)/10000)
,c(replicate(n-k, sample(10000,1)/10000)) )
}
while (sum(temp<adaptation_screen)==0){
if (n-k ==0){
temp <- c(replicate(k, sample(max,1)/10000))
}
else if(k==0){
temp <- c(replicate(n, sample(10000,1)/10000) )
}
else{
temp <- c(replicate(k, sample(max,1)/10000)
,c(replicate(n-k, sample(10000,1)/10000)) )
}
}
return(temp)
}
runPowerAnalysis <- function(reps, alpha_1, n, power_vec, speciesVec){
emp_p_null_dat <- t(replicate(10000, generateNullData(alpha_1, 1, 1, n)))
emp_p_null_dat_unscaled <- t(apply(emp_p_null_dat ,1, order_stats_p_values_unscaled))
null_pMax_cor_unscaled <- cor( emp_p_null_dat_unscaled )
result_count= 0
output_DFs = list()
for (GEA_power in c(0.05, 1:9/10) ){
print(GEA_power)
topHits = 500/GEA_power
for (nSpecies in speciesVec){
print(nSpecies)
result_count = result_count +1
test_data <- t(replicate(reps,
generateAltData(alpha_1,
topHits,
nSpecies,
n)))
test_data <- t(apply(test_data, 1, sort))
test_data_order_stats_unscaled <- t(apply(test_data ,1, order_stats_p_values_unscaled))
test_data_order_stats_unscaled
binom_results = rep(-1, reps)
binom_adj_results = rep(-1, reps)
tippett_results = rep(-1, reps)
# ordmeta_all_results = rep(-1, reps)
# ordmeta_2plus_results = rep(-1, reps)
for (i in 1:reps){
print(i)
# print(test_data[i,])
unscaled_p_value_vector = test_data_order_stats_unscaled[i,]
temp_binom <- binom.test( sum(test_data[i,]<alpha_1), n, alpha_1, , alternative='greater')$p.value
temp_binom_adj <- binom.test( sum(test_data[i,]<alpha_1)-1, n-1, alpha_1, , alternative='greater')$p.value
temp_tippett <- tippett(unscaled_p_value_vector, adjust = "empirical", R = null_pMax_cor_unscaled, side = 1, size = 10000)$p
# temp_ordmeta_all <- ordmeta(test_data[i,])$p
# temp_ordmeta_2plus <- ordmeta(sort(test_data[i,])[2:length(test_data[i,])])$p
print(c(temp_tippett) )
binom_results[i] = temp_binom
binom_adj_results[i] = temp_binom_adj
tippett_results[i] = temp_tippett
# ordmeta_all_results[i] = temp_ordmeta_all
# ordmeta_2plus_results[i] = temp_ordmeta_2plus
}
output_DFs[[result_count]] = data.frame(GEA_Power=GEA_power,
nSpeciesConvergent=nSpecies,
power_1_binomial=sum(binom_results<power_vec[1])/reps,
power_1_binomial_adj=sum(binom_adj_results<power_vec[1])/reps,
power_1_tippett=sum(tippett_results<power_vec[1])/reps,
# power_1_ordmeta_all=sum(ordmeta_all_results<power_vec[1])/reps,
# power_1_ordmeta_2plus=sum(ordmeta_2plus_results<power_vec[1])/reps,
power_2_binomial=sum(binom_results<power_vec[2])/reps,
power_2_binomial_adj=sum(binom_adj_results<power_vec[2])/reps,
power_2_tippett=sum(tippett_results<power_vec[2])/reps)
# power_2_ordmeta_all=sum(ordmeta_all_results<power_vec[2])/reps,
# power_2_ordmeta_2plus=sum(ordmeta_2plus_results<power_vec[2])/reps)
}
}
output_temp <- do.call(rbind, output_DFs)
return(output_temp)
}
#################
### 7 species
Reps = 1000
Alpha_1 = 0.05
N = 7
Power_vec=c(0.01, 0.003757)
SpeciesVec = c(1:7)
#output_df_n7 <- runPowerAnalysis( Reps, Alpha_1, N, Power_vec, SpeciesVec )
#write.csv(output_df_n7,"~/UBC/GEA/pMax/Analysis/7_species_alpha_0.05_adap.csv")
output_df_n7 <- read.csv("~/UBC/GEA/pMax/Analysis/7_species_alpha_0.05_adap.csv")
#output_df_n7_alpha_1_01 <- runPowerAnalysis( Reps, 0.01, N, Power_vec, SpeciesVec )
#write.csv(output_df_n7_alpha_1_01,"~/UBC/GEA/pMax/Analysis/7_species_alpha_0.01_adap.csv")
output_df_n7_alpha_1_01 <- read.csv("~/UBC/GEA/pMax/Analysis/7_species_alpha_0.01_adap.csv")
#################
## N = 30
Reps_n30 = 1000
Alpha_1_n30 = 0.01
N_n30 = 30
Power_vec_n30=c(0.01, 0.003318)
SpeciesVec_n30 = c(1,2,5,10,20,30)
#output_df_n30 <- runPowerAnalysis( Reps_n30, Alpha_1_n30, N_n30, Power_vec_n30, SpeciesVec_n30 )
#write.csv(output_df_n30,"~/UBC/GEA/pMax/Analysis/30_species_alpha_0.01_adap.csv")
output_df_n30 <- read.csv("~/UBC/GEA/pMax/Analysis/30_species_alpha_0.01_adap.csv")
#output_df_n30$nSpeciesConvergent <- rep(c(1,2,5,10,20,30),10)
#################
## N = 30 - for sam
Reps_n30_s = 1000
Alpha_1_n30_s = 0.01
N_n30_s = 30
Power_vec_n30_s=c(0.03615, 0.003318)
SpeciesVec_n30_s = c(1,3,5,7,9,11)
#output_df_n30_s <- runPowerAnalysis( Reps_n30_s, Alpha_1_n30_s, N_n30_s, Power_vec_n30_s, SpeciesVec_n30_s )
#write.csv(output_df_n30_s,"~/UBC/GEA/pMax/Analysis/30_species_alpha_0.01_adap_forSam.csv")
output_df_n30_s <- read.csv("~/UBC/GEA/pMax/Analysis/30_species_alpha_0.01_adap_forSam.csv")
#################
## N = 3
Reps_n3 = 1000
Alpha_1_n3 = 0.05
N_n3 = 3
Power_vec_n3=c(0.0073)
SpeciesVec_n3 = c(0,1,2,3)
#output_df_n3 <- runPowerAnalysis( Reps_n3, Alpha_1_n3, N_n3, Power_vec_n3, SpeciesVec_n3 )
#write.csv(output_df_n3,"~/UBC/GEA/pMax/Analysis/3_species_alpha_0.05_adap.csv")
output_df_n3 <- read.csv("~/UBC/GEA/pMax/Analysis/3_species_alpha_0.05_adap.csv")
#################
## N = 2
Reps_n2 = 1000
Alpha_1_n2 = 0.05
N_n2 = 2
Power_vec_n2 = c(0.0073)
SpeciesVec_n2 = c(0,1,2)
output_df_n2 <- runPowerAnalysis( Reps_n2, Alpha_1_n2, N_n2, Power_vec_n2, SpeciesVec_n2 )
write.csv(output_df_n2,"~/UBC/GEA/pMax/Analysis/2_species_alpha_0.05_adap.csv")
output_df_n2 <- read.csv("~/UBC/GEA/pMax/Analysis/2_species_alpha_0.05_adap.csv")
###########
# Plot the data
#
# Start with n=7
# With Binomial Test
output_df_n7$nSpeciesConvergent <- factor(output_df_n7$nSpeciesConvergent,
levels = c(1,2,3,4,5,6,7),
labels = c("1/7",
"2/7",
"3/7",
"4/7",
"5/7",
"6/7",
"7/7"))
power_1_plot_n7 <- ggplot(data = output_df_n7)+
geom_hline(aes(yintercept = Power_vec[2]))+
geom_line(aes(x = GEA_Power,
y = power_2_tippett,
col = as.factor(nSpeciesConvergent),
lty = "PicMin"),
lwd=1)+
geom_line(aes(x = GEA_Power,
y = power_2_binomial,
col = as.factor(nSpeciesConvergent),
lty = "Binomial Test"),
lwd = 1)+
# ggtitle("Adaptation Outliers Identified\nUsing 95th Percentile")+
scale_y_continuous(expression("Probability of Rejecting Null Hypothesis ("*alpha[Repeated]*"=0.01)"),
limits = c(0,1))+
scale_x_continuous("Power of Genome Scan",
breaks = 1:9/10)+
scale_colour_manual("Number of\nLineages\nWhere the\nGene is Causal", values = colorBlindBlack8)+
scale_linetype_manual("Statistical Test", values = c(2,1))+
theme_bw()+
theme(
plot.title = element_text(hjust = 0.5)
)
### Now n=30 with binomial
output_df_n30$nSpeciesConvergent <- factor(output_df_n30$nSpeciesConvergent,
levels = c(1,2,5,10,20,30),
labels = c("1/30",
"2/30",
"5/30",
"10/30",
"20/30",
"30/30"))
power_1_plot_n30_adap_01 <- ggplot(data = output_df_n30)+
geom_hline(aes(yintercept = Power_vec_n30[2]))+
geom_line(aes(x = GEA_Power,
y = power_2_tippett,
col = as.factor(nSpeciesConvergent),
lty = "PicMin"),
lwd=1)+
geom_line(aes(x = GEA_Power,
y = power_2_binomial,
col = as.factor(nSpeciesConvergent),
lty = "Binomial Test"),
lwd = 1)+
# ggtitle("Adaptation Outliers Identified\nUsing 99th Percentile")+
scale_y_continuous(expression("Probability of Rejecting Null Hypothesis ("*alpha[Repeated]*"=0.00332)"),
limits = c(0,1))+
scale_x_continuous("Power of Genome Scan",
breaks = 1:9/10)+
scale_colour_manual("Number of\nLineages\nWhere the\nGene is Causal", values = colorBlindBlack8)+
scale_linetype_manual("Statistical Test", values = c(2,1))+
theme_bw()+
theme(
plot.title = element_text(hjust = 0.5)
)
# Supp Figure 5
S5 <- ggarrange(power_1_plot_n7,
power_1_plot_n30_adap_01,
ncol = 1,
nrow = 2,
labels = "AUTO")
png("~/UBC/GEA/pMax/writeUp/Plots/SuppFigure5.png",width = 500, height = 700)
print(S5)
dev.off()
# Same plot, but without the Binomial Test
F1_power_1_plot_n7 <- ggplot(data = output_df_n7_alpha_1_01)+
geom_hline(aes(yintercept = Power_vec[1]))+
geom_line(aes(x = GEA_Power,
y = power_2_tippett,
col = as.factor(nSpeciesConvergent)),
lwd=1)+
scale_y_continuous(expression("Power ("*alpha[Repeated]*"=0.01)"),
limits = c(0,1))+
scale_x_continuous("Power of Genome Scan",
breaks = 1:9/10)+
scale_colour_manual("Number of\nLineages\nWhere the\nGene is Causal", values = colorBlindBlack8)+
theme_bw()+
theme(
plot.title = element_text(hjust = 0.5)
)
### Now n=30 with binomial
F1_power_1_plot_n30_adap_01 <- ggplot(data = output_df_n30)+
geom_hline(aes(yintercept = Power_vec_n30[1]))+
geom_line(aes(x = GEA_Power,
y = power_2_tippett,
col = as.factor(nSpeciesConvergent)),
lwd=1)+
scale_y_continuous(expression("Power ("*alpha[Repeated]*"=0.01)"),
limits = c(0,1))+
scale_x_continuous("Power of Genome Scan",
breaks = 1:9/10)+
scale_colour_manual("Number of\nLineages\nWhere the\nGene is Causal", values = colorBlindBlack8)+
theme_bw()+
theme(
plot.title = element_text(hjust = 0.5)
)
# Figure 2
Figure2 <- ggarrange(F1_power_1_plot_n7,
F1_power_1_plot_n30_adap_01,
ncol = 1,
nrow = 2,
labels = "AUTO")
pdf("~/UBC/GEA/pMax/writeUp/Plots/Figure2.pdf",width = 5, height = 7)
print(Figure2)
dev.off()
###################
### Num Species Convergent
###
###
###
###
###
n = 7
alpha_1 = 0.05
emp_p_null_dat <- t(replicate(10000, generateNullData(alpha_1, 1, 1, n)))
emp_p_null_dat_unscaled <- t(apply(emp_p_null_dat ,1, order_stats_p_values_unscaled))
null_pMax_cor_unscaled <- cor( emp_p_null_dat_unscaled )
reps = 200
result_count= 1
output_DFs = list()
threshold= 0.05
for (GEA_power in c(0.2, 0.4, 0.6, 0.8) ){
topHits = 500/GEA_power
for (nSpecies in c(3,5,7)){
# for (nSpecies in c(3)){
for (i in 1:reps){
pass_test = FALSE
while (pass_test == FALSE){
test_data <- generateAltData(alpha_1,
topHits,
nSpecies,
n)
test_data_order_stats_unscaled <- order_stats_p_values_unscaled(test_data)
temp_binom <- binom.test( sum(test_data<alpha_1), n, alpha_1, , alternative='greater')$p.value
# temp_binom_adj <- binom.test( sum(test_data[i,]<alpha_1)-1, n-1, alpha_1, , alternative='greater')$p.value
temp_tippett <- tippett(test_data_order_stats_unscaled, adjust = "empirical", R = null_pMax_cor_unscaled, side = 1)$p
if ((temp_binom<threshold)&(temp_tippett<threshold)){
binom_count = sum(test_data<alpha_1)
PicMin_estimate = which.min( test_data_order_stats_unscaled )+1
print(test_data)
print(c(GEA_power, nSpecies, i, binom_count, PicMin_estimate))
print("'")
output_DFs[[result_count]] = data.frame(GEA_Power=GEA_power,
nSpeciesConvergent=nSpecies,
binomial_estimate=binom_count,
PicMin_estimate=PicMin_estimate)
result_count = result_count +1
pass_test = TRUE
}
}
}
}
}
#output_temp <- do.call(rbind, output_DFs)
#write.csv(output_temp, "~/UBC/GEA/pMax/Analysis/convergenceConfigurations_n7_a0.05.csv")
output_temp <- read.csv("~/UBC/GEA/pMax/Analysis/convergenceConfigurations_n7_a0.05.csv")
data <- melt(output_temp, id = c("GEA_Power", "nSpeciesConvergent", "X"))
data$variable <- factor( data$variable,
levels = c("binomial_estimate",
"PicMin_estimate"),
labels = c(expression("Simple Threshold ("*italic(ep)*"-value < 0.05)"),
expression(italic("PicMin")*" Estimate"))
)
data$GEA_Power <- paste("Genome Scan Power = ", data$GEA_Power, sep = "")
safe_colorblind_palette <- c("#88CCEE", "#CC6677", "#DDCC77", "#117733", "#332288", "#AA4499",
"#44AA99", "#999933", "#882255", "#661100", "#6699CC", "#888888")
colourPal <- safe_colorblind_palette[c(3,7,11)]
data$indicator <- data$value == data$nSpeciesConvergent
tbl <- with(data, table(nSpeciesConvergent, variable, GEA_Power, value))
plot_data <- as.data.frame(tbl)
plot_data$indicator <- as.numeric(as.character(plot_data$value)) == as.numeric(as.character(plot_data$nSpeciesConvergent))
plot_data$nSpeciesConvergent_lab <- paste(plot_data$nSpeciesConvergent, " Lineages With Causal Gene", sep = "")
parse.labels <- function(x) parse(text = x)
config_plot <- ggplot(plot_data, aes(value, Freq, fill = variable, col = indicator)) +
geom_col(position = 'dodge')+
scale_color_manual(values = c("white","black"))+
scale_x_discrete("Estimate of the Number of Lineages Where the Gene is Causal",
breaks = c(1,2,3,4,5,6,7)) +
scale_y_continuous(limits = c(0,200))+
#coord_cartesian(clip = "off") +
scale_fill_manual("", values = colourPal, labels = parse.labels)+
guides(alpha = "none", colour = "none")+
facet_grid(nSpeciesConvergent_lab~GEA_Power)+
theme_bw()+
theme(
axis.title.y = element_blank() ,
axis.title.x = element_text( hjust = 0.5),
strip.background = element_blank(),
strip.text.y = element_text(vjust = 1),
legend.position = "bottom"
)
ggsave("~/UBC/GEA/pMax/writeUp/Plots/Figure3.pdf",config_plot,
width = 8,
height = 7)
ggsave("~/UBC/GEA/pMax/writeUp/Plots/Figure3.png", config_plot,
device = png,
units = "in",
width = 8, height = 7,
res = 1000)
###########
#### Config plot n=30
n = 30
alpha_1 = 0.01
emp_p_null_dat <- t(replicate(10000, generateNullData(alpha_1, 1, 1, n)))
emp_p_null_dat_unscaled <- t(apply(emp_p_null_dat ,1, order_stats_p_values_unscaled))
null_pMax_cor_unscaled <- cor( emp_p_null_dat_unscaled )
reps = 200
result_count= 1
output_DFs = list()
threshold= 0.045
for (GEA_power in c(0.2, 0.4, 0.6, 0.8) ){
topHits = 500/GEA_power
for (nSpecies in c(3,5,7,9,11)){
# for (nSpecies in c(3)){
for (i in 1:reps){
pass_test = FALSE
while (pass_test == FALSE){
test_data <- generateAltData(alpha_1,
topHits,
nSpecies,
n)
test_data_order_stats_unscaled <- order_stats_p_values_unscaled(test_data)
temp_binom <- binom.test( sum(test_data<alpha_1), n, alpha_1, , alternative='greater')$p.value
# temp_binom_adj <- binom.test( sum(test_data[i,]<alpha_1)-1, n-1, alpha_1, , alternative='greater')$p.value
temp_tippett <- tippett(test_data_order_stats_unscaled, adjust = "empirical", R = null_pMax_cor_unscaled, side = 1)$p
if ((temp_binom<threshold)&(temp_tippett<threshold)){
binom_count = sum(test_data<alpha_1)
PicMin_estimate = which.min( test_data_order_stats_unscaled )+1
print(test_data)
print(c(GEA_power, nSpecies, i, binom_count, PicMin_estimate))
print("'")
output_DFs[[result_count]] = data.frame(GEA_Power=GEA_power,
nSpeciesConvergent=nSpecies,
binomial_estimate=binom_count,
PicMin_estimate=PicMin_estimate)
result_count = result_count +1
pass_test = TRUE
}
}
}
}
}
output_temp <- do.call(rbind, output_DFs)
data <- melt(output_temp, id = c("GEA_Power", "nSpeciesConvergent"))
data$variable <- factor( data$variable,
levels = c("binomial_estimate",
"PicMin_estimate"),
labels = c(expression(atop("Simple Threshold", paste("("*italic(ep)*"-value <0.05)"))),
expression(atop(italic("PicMin"), paste("Estimate")))
)
)
data$GEA_Power <- paste("Genome Scan Power = ", data$GEA_Power, sep = "")
expression(atop("Simple Threshold", paste("("*italic(ep)*"-value <0.05)")))
safe_colorblind_palette <- c("#88CCEE", "#CC6677", "#DDCC77", "#117733", "#332288", "#AA4499",
"#44AA99", "#999933", "#882255", "#661100", "#6699CC", "#888888")
colourPal <- safe_colorblind_palette[c(3,7,11)]
data$indicator <- data$value == data$nSpeciesConvergent
tbl <- with(data, table(nSpeciesConvergent, variable, GEA_Power, value))
plot_data <- as.data.frame(tbl)
plot_data$indicator <- as.numeric(as.character(plot_data$value)) == as.numeric(as.character(plot_data$nSpeciesConvergent))
plot_data$nSpeciesConvergent_lab <- paste(plot_data$nSpeciesConvergent, " Species Using Gene", sep = "")
plot_data$nSpeciesConvergent_lab <- factor(plot_data$nSpeciesConvergent_lab,
levels = c("3 Species Using Gene",
"5 Species Using Gene",
"7 Species Using Gene",
"9 Species Using Gene",
"11 Species Using Gene"))
config_plot <- ggplot(plot_data, aes(value, Freq, fill = variable)) +
geom_col(position = 'dodge')+
geom_col(data= plot_data[plot_data$indicator == TRUE,], aes(fill = variable),position = 'dodge',col ="black")+
scale_x_discrete("Estimate of the Number of Lineages Using the Gene",
breaks = c(5,10,15,20,25,30)) +
scale_y_continuous(limits = c(0,200))+
#coord_cartesian(clip = "off") +
scale_fill_manual("", values = colourPal)+
guides(alpha = "none", colour = "none")+
facet_grid(nSpeciesConvergent_lab~GEA_Power)+
theme_bw()+
theme(
axis.title.y = element_blank() ,
axis.title.x = element_text( hjust = 0.5),
strip.background = element_blank(),
strip.text.y = element_text(vjust = 1),
legend.position = "bottom"
)
ggsave("~/UBC/GEA/pMax/Analysis/convergenceConfigurations_n30_a0.05_t0.045_s.pdf",config_plot,
width = 14,
height = 10)
######
#
# Full genome comparison
#
#
nGenes <- 10000
nSpecies <- 7
nHits <- 100
# First, simulate p-values for each species
speciesEmpiricalP <- function(nGenes, nHits, a, b){
species_p_values <- c( rbeta( nHits, a,b),
runif(nGenes-nHits) )
species_empirical_p_values <- rank(species_p_values)/nGenes
return(species_empirical_p_values)
}
sum(rbeta(1000, 0.2,5)<0.05)
GEA_power_list <- list(
list(a=0.8, b = 5, power=0.3), # ~30% power
# list(a=0.66, b = 5, power=0.4), # ~40% power
list(a=0.5, b = 5, power=0.5), # ~50% power
# list(a=0.4, b = 5, power=0.6), # ~60% power
list(a=0.3, b = 5, power = 0.7), # ~70% power
# list(a=0.2, b = 5, power = 0.8), # ~70% power
list(a=0.1, b = 5, power = 0.9) # ~90% power
)
alpha_a <- 0.05
# Run 10,000 replicate simulations of this situation and build the correlation matrix
emp_p_null_dat <- t(replicate(40000, generateNullData(alpha_a, 0.5, 3, 7)))
# Calculate the order statistics p-values for each simulation
emp_p_null_dat_unscaled <- t(apply(emp_p_null_dat ,1, order_stats_p_values_unscaled))
# Use those p-values to construct the correlation matrix
null_pMax_cor_unscaled <- cor( emp_p_null_dat_unscaled )
output_DF_list <- list()
result_count=0
for (k in c(3,5,7)){
# for (k in c(7)){
for (g in GEA_power_list){
for (rep in 1:10){
print(c(k,g$power,rep))
result_count = result_count+1
if (k < 7){
dataSet <- cbind( c(replicate(k, speciesEmpiricalP(nGenes, nHits, g$a, g$b)) ),
c(replicate(7-k, speciesEmpiricalP(nGenes, 0, 1, 1) ) ))
}
else if (k == 7){
dataSet <- cbind( replicate(k, speciesEmpiricalP(nGenes, nHits, g$a, g$b)) )
}
adap_screen_vector <- apply(dataSet<0.05, 1, sum)!=0
gene_names <- c(1:nGenes)[adap_screen_vector]
screenedDataSet <- dataSet[adap_screen_vector, ]
registerDoParallel(numCores)
output_p_values<- foreach (i=1:nrow(screenedDataSet), .combine=c) %dopar% {
# output_p_values<- foreach (i=1:100, .combine=c) %dopar% {
PicMin(screenedDataSet[i,], null_pMax_cor_unscaled)$p
}
q_values <- p.adjust( output_p_values, method = "fdr" )
temp_df <- as.data.frame(screenedDataSet)
temp_df$gene_id = gene_names
temp_df$q <- q_values
true_positives <- sum((temp_df$q<0.05)&(temp_df$gene_id<=nHits))
false_positives <- sum((temp_df$q<0.05)&(temp_df$gene_id>nHits))
output_DF_list[[result_count]] = data.frame(GEA_Power=g$power,
rep = rep,
nSpeciesConvergent=k,
true_positives=true_positives,
false_positives=false_positives)
}
}
}
temp <- do.call(rbind, output_DF_list)
#write.csv(temp, "~/UBC/GEA/pMax/Analysis/genome_analysis_3-7.csv")
temp <- read.csv("~/UBC/GEA/pMax/Analysis/genome_analysis_3-7.csv")
temp$GEA_Power <- as.character(temp$GEA_Power)
temp$nSpeciesConvergent <- as.character(temp$nSpeciesConvergent)
temp_melt <- melt(temp,
id = c("GEA_Power", "rep", "nSpeciesConvergent", "X"))
temp_melt$variable <- factor(temp_melt$variable,
levels = c("true_positives","false_positives"),
labels = c("True Positives", "False Positives"))
genome_fdr_plot <- ggplot(data = temp_melt, aes(x = as.factor(GEA_Power), y = value, fill = nSpeciesConvergent,col = nSpeciesConvergent))+
geom_boxplot(alpha = 0.5)+
scale_y_continuous("Number of Genes Identified\n(q<0.05)",
limits = c(0,100))+
scale_x_discrete("Genome Scan Power")+
facet_wrap(~variable)+
scale_fill_manual("Number of\nLineages\nWhere the\nGene is Causal"
,values = colorBlindBlack8[c(3,5,7)])+
scale_color_manual("Number of\nLineages\nWhere the\nGene is Causal"
,values = colorBlindBlack8[c(3,5,7)])+
theme_bw()+
theme(
strip.background = element_blank(),
strip.text = element_text(size = 12)
)
ggsave("~/UBC/GEA/pMax/writeUp/Plots/Plot4.pdf",
genome_fdr_plot,
width = 8,
height = 4)
ggsave("~/UBC/GEA/pMax/writeUp/Plots/Plot4.png",
genome_fdr_plot,
units = "in",
device = "png",
width = 8,
height = 4)
############
### Alpha_a threshold
###
runPowerAnalysis_alpha1 <- function(reps, GEA_power_vec, n, power_vec, speciesVec){
output_DFs = list()
result_count= 0
for (alpha_1 in c(10,50,100,500,1000)/10000 ){
emp_p_null_dat <- t(replicate(10000, generateNullData(alpha_1, 1, 1, n)))
emp_p_null_dat_unscaled <- t(apply(emp_p_null_dat ,1, order_stats_p_values_unscaled))
null_pMax_cor_unscaled <- cor( emp_p_null_dat_unscaled )
for (GEA_power in GEA_power_vec){
print(GEA_power)
topHits = 500/GEA_power
for (nSpecies in speciesVec){
print(nSpecies)
result_count = result_count +1
test_data <- t(replicate(reps,
generateAltData(alpha_1,
topHits,
nSpecies,
n)))
test_data <- t(apply(test_data, 1, sort))
test_data_order_stats_unscaled <- t(apply(test_data ,1, order_stats_p_values_unscaled))
test_data_order_stats_unscaled
# binom_results = rep(-1, reps)
# binom_adj_results = rep(-1, reps)
tippett_results = rep(-1, reps)
# ordmeta_all_results = rep(-1, reps)
# ordmeta_2plus_results = rep(-1, reps)
for (i in 1:reps){
print(i)
# print(test_data[i,])
unscaled_p_value_vector = test_data_order_stats_unscaled[i,]
# temp_binom <- binom.test( sum(test_data[i,]<alpha_1), n, alpha_1, , alternative='greater')$p.value
# temp_binom_adj <- binom.test( sum(test_data[i,]<alpha_1)-1, n-1, alpha_1, , alternative='greater')$p.value
temp_tippett <- tippett(unscaled_p_value_vector, adjust = "empirical", R = null_pMax_cor_unscaled, side = 1, size = 10000)$p
# temp_ordmeta_all <- ordmeta(test_data[i,])$p
# temp_ordmeta_2plus <- ordmeta(sort(test_data[i,])[2:length(test_data[i,])])$p
print(c(temp_tippett) )
# binom_results[i] = temp_binom
# binom_adj_results[i] = temp_binom_adj
tippett_results[i] = temp_tippett
# ordmeta_all_results[i] = temp_ordmeta_all
# ordmeta_2plus_results[i] = temp_ordmeta_2plus
}
output_DFs[[result_count]] = data.frame(alpha_a=alpha_1,
nSpeciesConvergent=nSpecies,
GEA_Power=GEA_power,
# power_1_binomial=sum(binom_results<power_vec[1])/reps,
# power_1_binomial_adj=sum(binom_adj_results<power_vec[1])/reps,
power_1_tippett=sum(tippett_results<power_vec[1])/reps,
# power_1_ordmeta_all=sum(ordmeta_all_results<power_vec[1])/reps,
# power_1_ordmeta_2plus=sum(ordmeta_2plus_results<power_vec[1])/reps,
# power_2_binomial=sum(binom_results<power_vec[2])/reps,
# power_2_binomial_adj=sum(binom_adj_results<power_vec[2])/reps,
power_2_tippett=sum(tippett_results<power_vec[2])/reps)
# power_2_ordmeta_all=sum(ordmeta_all_results<power_vec[2])/reps,
# power_2_ordmeta_2plus=sum(ordmeta_2plus_results<power_vec[2])/reps)
print( output_DFs[[result_count]])
}
}
}
output_temp <- do.call(rbind, output_DFs)
return(output_temp)
}
#######
### 7 species
Reps = 1000
GEA_power_vec = c(0.2,0.50, 0.7, 0.9)
N = 7
Power_vec=c(0.05, 0.01)
SpeciesVec = c(1:7)
#output_df_n7_alpha_screen <- runPowerAnalysis_alpha1( Reps, GEA_power_vec, N, Power_vec, SpeciesVec )
output_df_n7_alpha_screen <- read.csv("~/UBC/GEA/pMax/Analysis/falsePositives_alphaAdapt_n7.csv")
#write.csv(output_df_n7_alpha_screen,
# "~/UBC/GEA/pMax/Analysis/falsePositives_alphaAdapt_n7.csv")
single_hit <- output_df_n7_alpha_screen[output_df_n7_alpha_screen$nSpeciesConvergent==1,]
single_hit_melt <- melt(single_hit, id = c("alpha_a","nSpeciesConvergent","GEA_Power", "X"))
#single_hit_melt <- melt(single_hit, id = c("alpha_a","nSpeciesConvergent","X"))
## Calculate Clopper-Pearson intervals
single_hit_melt$lower_p <-qbeta(0.05/2,
single_hit_melt$value*Reps,
Reps - single_hit_melt$value*Reps +1)
single_hit_melt$upper_p <-qbeta(1-0.05/2,
single_hit_melt$value*Reps+1,
Reps - single_hit_melt$value*Reps)
single_hit_melt$variable <- factor(single_hit_melt$variable,
levels = c("power_1_tippett","power_2_tippett"),
labels = c(expression(alpha[Repeated]*" = 0.05"),
expression(alpha[Repeated]*" = 0.01")))
lineref_df <- data.frame(value = c(0.05,0.01), variable = c("power_1_tippett","power_2_tippett"))
lineref_df$variable <- factor(lineref_df$variable,
levels = c("power_1_tippett","power_2_tippett"),
labels = c(expression(alpha[Repeated]*" = 0.05"),
expression(alpha[Repeated]*" = 0.01")))
##############
### 30 species
###
Reps = 1000
GEA_power_vec = c(0.2, 0.50, 0.7, 0.9)
N = 30
Power_vec=c(0.05, 0.01)
SpeciesVec = c(1)
#output_df_n30_alpha_screen <- runPowerAnalysis_alpha1( Reps, GEA_power_vec, N, Power_vec, SpeciesVec )
output_df_n30_alpha_screen <- read.csv("~/UBC/GEA/pMax/Analysis/falsePositives_alphaAdapt_n30.csv")
#write.csv(output_df_n30_alpha_screen,
# "~/UBC/GEA/pMax/Analysis/falsePositives_alphaAdapt_n30.csv")
single_hit_n30 <- output_df_n30_alpha_screen[output_df_n30_alpha_screen$nSpeciesConvergent==1,]
single_hit_n30_melt <- melt(single_hit_n30, id = c("alpha_a","nSpeciesConvergent","GEA_Power", "X"))
#single_hit_melt <- melt(single_hit, id = c("alpha_a","nSpeciesConvergent","X"))
## Calculate Clopper-Pearson intervals
single_hit_n30_melt$lower_p <-qbeta(0.05/2,
single_hit_n30_melt$value*Reps,
Reps - single_hit_n30_melt$value*Reps +1)
single_hit_n30_melt$upper_p <-qbeta(1-0.05/2,
single_hit_n30_melt$value*Reps+1,
Reps - single_hit_n30_melt$value*Reps)
single_hit_n30_melt$variable <- factor(single_hit_n30_melt$variable,
levels = c("power_1_tippett","power_2_tippett"),
labels = c(expression(alpha[Repeated]*" = 0.05"),
expression(alpha[Repeated]*" = 0.01")))
single_hit_n30_melt$n <- "30"
single_hit_melt$n <- "7"
plot_single_hit_data <- rbind(single_hit_melt,single_hit_n30_melt)
plot_single_hit_data$n <- factor( plot_single_hit_data$n ,
levels = c("7","30"),
labels = c(expression(7*" Species"),
expression(30*" Species")))
alpha_adapt_FP_plot <- ggplot(data = plot_single_hit_data)+
geom_point(aes( x= as.factor(GEA_Power),
y= value,
col = as.factor(alpha_a)),
position=position_dodge(width=0.25))+
geom_errorbar(aes( x= as.factor(GEA_Power),
y= value,
col = as.factor(alpha_a),
ymin = lower_p,
ymax = upper_p),
position=position_dodge(width=0.25),
width = 0.01)+
scale_x_discrete("Genome Scan Power")+
scale_y_continuous("Proportion of False Positives",
limits =c(0,0.15))+
geom_hline(data = lineref_df, aes(yintercept = value), lty = 2)+
facet_grid(n~variable,
scale = "free_y",
label=label_parsed)+
scale_color_brewer(expression(alpha[Adapt]),
palette="Dark2")+
theme_bw()+
theme(
strip.background = element_blank(),
strip.text.x = element_text(size = 12)
)
ggsave("~/UBC/GEA/pMax/Analysis/falsePositives_alphaAdapt.pdf", alpha_adapt_FP_plot,
width = 7, height = 5)
ggsave("~/UBC/GEA/pMax/Analysis/falsePositives_alphaAdapt.png", device = png,
alpha_adapt_FP_plot,
units = "in",
width = 7, height = 5,
res = 1000)
multi_n_data <- output_df_n7_alpha_screen[output_df_n7_alpha_screen$nSpeciesConvergent>1,]
multi_n_data_melt <- melt(multi_n_data,
id = c("X","alpha_a","nSpeciesConvergent","GEA_Power"))
label_vec <- c(expression(2*" Species Convergent"),
expression(3*" Species Convergent"),
expression(4*" Species Convergent"),
expression(5*" Species Convergent"),
expression(6*" Species Convergent"),
expression(7*" Species Convergent"))
multi_n_data_melt$variable <- factor(multi_n_data_melt$variable,
levels = c("power_1_tippett","power_2_tippett"),
labels = c("alpha[Repeated]*' = 0.05'",
"alpha[Repeated]*' = 0.01'"))
multi_n_data_melt$n <- factor(multi_n_data_melt$nSpeciesConvergent,
levels = 2:7,
labels = label_vec)
#paste(multi_n_data_melt$nSpeciesConvergent, "Species Convergent")
alpha_adapt_TP_plot <- ggplot(data = multi_n_data_melt)+
geom_line(aes( x= as.factor(GEA_Power),
y= value,
col = as.factor(alpha_a),
group = as.factor(alpha_a)),
lwd= 1)+
facet_grid(n~variable,
labeller = label_parsed)+
scale_x_discrete("Genome Scan Power")+
scale_y_continuous("Probability of Rejecting Null Hypothesis")+
scale_color_brewer(expression(alpha[Adapt]),
palette="Dark2")+
theme_bw()+
theme(
strip.background = element_blank(),
strip.text.x = element_text(size = 12)
)
ggsave("~/UBC/GEA/pMax/Analysis/truePositives_alphaAdapt.pdf", alpha_adapt_TP_plot,
width = 7, height = 12)
ggsave("~/UBC/GEA/pMax/Analysis/truePositives_alphaAdapt.png", alpha_adapt_TP_plot,
device = png,
units = "in",
width = 7, height = 12,
res = 1000)
###################
# p-value distributions under the null
#' @title Generate data under the null hypothesis
#' @param adaptation_screen The threshold used to determine adaptation
#' @param a the 'a' parameter of a beta distribution of p-values for the false null
#' @param b the 'b' parameter of a beta distribution of p-values for the false null
#' @param n the number of species in the test
#' @param genes the number of genes in the genome use to calculate empirical p-values
#' @importFrom "stats" "rbeta"
GenerateNullData <- function(adaptation_screen, a, b, n, genes){
temp <- c( rbeta(1,a,b),replicate(n-1, sample(genes,1)/genes) )
while (sum(temp<adaptation_screen)==0){
temp <- c( rbeta(1,a,b),replicate(n-1, sample(genes,1)/genes) )
}
return(temp)
}
replicates = 100
n=7
results_DFs <- list()
count = 0
for (alpha_adapt in c(0.005, 0.01, 0.05, 1.0)){
null_data_for_mat <- t(replicate(40000, GenerateNullData(alpha_adapt, 0.5, 3, n, 10000)))
emp_p_null_dat_unscaled <- t(apply(null_data_for_mat,
1,
order_stats_p_values_unscaled))
# Use those p-values to construct the correlation matrix
null_pMax_cor_unscaled <- cor( emp_p_null_dat_unscaled )
for (maxP in c(600, 1000, 2000)){
count = count + 1
print(c(alpha_adapt, maxP))
null_data <- t( replicate( replicates,
generateAltData(alpha_adapt,
maxP,
1,
n) ) )
null_data_order_stats <- t(apply(null_data_for_mat,
1,
order_stats_p_values_unscaled))
result_vector = rep(0,
replicates)
print("!")
for (s in seq_len(replicates)){
print(s)
temp_tippett <- tippett(null_data_order_stats[s,],
adjust = "empirical",
R = null_pMax_cor_unscaled,
side = 1,
size = 10000)$p
result_vector[s] = temp_tippett
}
results_DFs[[count]] = data.frame(p_vals = result_vector,
alpha_adapt = rep(alpha_adapt, replicates),
maxP = rep(maxP, replicates))
}
}
histo_data <- do.call( rbind, results_DFs )
histo_data$alpha_adapt <- factor(histo_data$alpha_adapt,
levels = c(0.005,0.01,0.05,1.0),
labels = c(expression(alpha[Adapt]*"=0.005"),
expression(alpha[Adapt]*"=0.01"),
expression(alpha[Adapt]*"=0.05"),
expression(alpha[Adapt]*"=1.0")))
histo_data$maxP <- 1/(histo_data$maxP/500)
histo_data$scan_power <- factor(histo_data$maxP,
levels = c(5/6, 0.5, 0.25),
labels = c(expression("Genome scan power"*" = 0.8333..."),
expression("Genome scan power"*" = 0.5"),
expression("Genome scan power"*" = 0.25")))
null_hypothesis_histogram <- ggplot(data = histo_data,
aes(x = p_vals))+
geom_histogram( binwidth = 0.05, boundary = 1, fill = "grey", col = "black" )+
facet_grid(scan_power~alpha_adapt,
label = label_parsed)+
scale_x_continuous(expression(italic("p")*"-value"))+
# scale_y_continuous("Count",
# limits = c(0,700))+
theme_bw()+
theme(
strip.text.x = element_text(size = 13),
strip.background = element_blank()
)
ggsave("~/UBC/GEA/pMax/Analysis/null_distribution_histogram_n7.pdf",
width = 9.50,
height = 6.50,
units = "in")
replicates = 10000
n=30
results_DFs <- list()
count = 0
for (alpha_adapt in c(0.005, 0.01, 0.05, 1.0)){
for (maxP in c(600, 1000, 2000)){
count = count + 1
null_data_for_mat <- t(replicate(40000, GenerateNullData(alpha_adapt, 0.5, 3, n, 10000)))
emp_p_null_dat_unscaled <- t(apply(null_data_for_mat,
1,
order_stats_p_values_unscaled))
# Use those p-values to construct the correlation matrix
null_pMax_cor_unscaled <- cor( emp_p_null_dat_unscaled )
null_data <- t( replicate( replicates,
generateAltData(alpha_adapt,
maxP,
1,
n) ) )
null_data_order_stats <- t(apply(null_data_for_mat,
1,
order_stats_p_values_unscaled))
result_vector = rep(0,
replicates)
for (s in seq_len(replicates)){
temp_tippett <- tippett(null_data_order_stats[s,],
adjust = "empirical",
R = null_pMax_cor_unscaled,
side = 1,
size = 10000)$p
result_vector[s] = temp_tippett
}
results_DFs[[count]] = data.frame(p_vals = result_vector,
alpha_adapt = rep(alpha_adapt, replicates),
maxP = rep(maxP, replicates))
}
}
histo_data <- do.call( rbind, results_DFs )
histo_data$alpha_adapt <- factor(histo_data$alpha_adapt,
levels = c(0.005,0.01,0.05,1.0),
labels = c(expression(alpha[Adapt]*"=0.005"),
expression(alpha[Adapt]*"=0.01"),
expression(alpha[Adapt]*"=0.05"),
expression(alpha[Adapt]*"=1.0")))
histo_data$maxP <- 1/(histo_data$maxP/500)
histo_data$scan_power <- factor(histo_data$maxP,
levels = c(5/6, 0.5, 0.25),
labels = c(expression("Genome scan power"*" = 0.8333..."),
expression("Genome scan power"*" = 0.5"),
expression("Genome scan power"*" = 0.25")))
null_hypothesis_histogram <- ggplot(data = histo_data,
aes(x = p_vals))+
geom_histogram( binwidth = 0.05, boundary = 1, fill = "grey", col = "black" )+
facet_grid(scan_power~alpha_adapt,
label = label_parsed)+
scale_x_continuous(expression(italic("p")*"-value"))+
scale_y_continuous("Count",
limits = c(0,700))+
theme_bw()+
theme(
strip.text.x = element_text(size = 13),
strip.background = element_blank()
)
ggsave("~/UBC/GEA/pMax/Analysis/null_distribution_histogram_n30.pdf",
width = 9.50,
height = 6.50,
units = "in")
TBooker/PicMin documentation built on June 6, 2023, 8:11 p.m.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
rm(list=ls())
colorBlindBlack8 <- c("#999999", "#E69F00", "#56B4E9", "#009E73",
"#F0E442", "#0072B2", "#D55E00", "#CC79A7")
library(rSymPy)
library(poolr)
library(reshape2)
library(ggplot2)
library(foreach)
library(doParallel)
library(ggpubr)
#' @title Beta probability
#' @param p p-value
#' @param i rank
#' @param n The number of inputs
#' @importFrom "stats" "pbeta" "qbeta"
F_i = function(p, i, n)
{
a = i
b = n-i+1
res = pbeta(q = p, shape1 = a, shape2 = b, lower.tail = T)
return(res)
}
#' @title ordmeta
#' @description Minimum Marginal P-value in joint order distribution
#' @param p A vector of p-values
#' @param is.onetail Logical. If set TRUE, p-values are combined without considering the direction of effect, and vice versa. Default: TRUE.
#' @param eff.sign A vector of signs of effect sizes. It works when is.onetail = FALSE
#' @importFrom "rSymPy" "Var" "sympy"
#' @return p : Combined p-value
#' @return optimal_rank : Optimal rank where minimum marginal p-value exists.
#' @return eff.p.idx : Index of effective p-values
#' @return MMP : Minimum marginal p-value
#' @return overall.eff.direction : The direction of combined effects.
#' @examples \donttest{ordmeta(p=c(0.01, 0.02, 0.8, 0.25), is.onetail=FALSE, eff.sign = c(1,1,1,-1))}
#' @export
ordmeta = function(p, is.onetail = TRUE, eff.sign=NULL)
{
direc = eff.sign
if(is.null(p)){stop("Input p-values are required.")}
if(!is.onetail & is.null(eff.sign)){stop("Input the direction of effects.")}
idx_na = which(is.na(p))
if(length(idx_na)>0){p = p[-idx_na]; eff.sign = eff.sign[-idx_na]}
ordmeta = function(p2)
{
ord = order(p2, decreasing = F)
pord = sort(p2, decreasing = F)
# get alpha = MIN(F_(i)(x)) {i={1..n}}
N = length(p2)
alpha = 1.01 # an arbitrary number larger than 1
for(i in 1:N)
{
alpha_temp = F_i(pord[i], i, N)
if(alpha_temp < alpha){idx_minimum = i; alpha = alpha_temp}
}
# symbolic integral
for(i in 1:N)
{
x = Var("x")
y = Var("y")
if(i==1)
{
templete = paste(i,"*integrate(1, (x, lob, y))")
lob = qbeta(p = alpha,shape1 = i, shape2 = N-i+1, lower.tail = T)
templete = gsub("lob", lob, templete)
}else if(i>1 & i<N){
integ = gsub(pattern = "y", replacement = "x", x = integ)
templete = paste(i, "*integrate(",integ,", (x, lob, y))")
lob = qbeta(p = alpha,shape1 = i, shape2 = N-i+1, lower.tail = T)
templete = gsub("lob", lob, templete)
}else if(i==N)
{
integ = gsub(pattern = "y", replacement = "x", x=integ)
templete = paste(i, "*integrate(",integ,", (x, lob, 1))")
lob = qbeta(p = alpha,shape1 = i, shape2 = N-i+1, lower.tail = T)
templete = gsub("lob", lob, templete)
}
#print(templete)
integ = sympy(templete)
}
res = 1-as.numeric(integ)
return(list(p=res, optimal_rank = idx_minimum, eff.p.idx = ord[1:idx_minimum], MMP = alpha))
}
if(is.onetail)
{
RES = ordmeta(p2 = p)
return(RES)
}else{
p1 = p2 = p
idx_pos = which(eff.sign >= 0)
idx_neg = which(eff.sign < 0)
p1[idx_pos] = p[idx_pos]/2
p1[idx_neg] = 1-p[idx_neg]/2
p2[idx_pos] = 1-p[idx_pos]/2
p2[idx_neg] = p[idx_neg]/2
RES1 = ordmeta(p2 = p1)
RES2 = ordmeta(p2 = p2)
if(RES1$p<=RES2$p){
RES = RES1; RES$overall.eff.direction = "+"
}else{
RES = RES2; RES$overall.eff.direction = "-"
}
RES$p = RES$p * 2
if(RES$p > 1.0){RES$p = 1.0}
return(RES)
}
}
p_value_adjustment <- function(p_vec){
p_adj = 1 - (1 - min(p_vec))^length(p_vec)
return(p_adj)
}
scale_p_values <- function(p_list, screen = 0.05){
# sort the list of $p$-values
p_sort_unscaled=sort(p_list)[2:length(p_list)]
p_sort <- (p_sort_unscaled - screen)/(1-screen)
return(p_sort)
}
order_stats_p_values_scaled <- function(p_list){
# sort the list of $p$-values
p_sort_unscaled <- sort(p_list)[2:length(p_list)]
p_sort <- (p_sort_unscaled - min(p_list))/(1-min(p_list))
#p_sort <- (p_sort_unscaled - min(p_list))/(1-min(p_list))
n = length(p_sort)
out_p = c()
for (j in 1:length(p_sort)){
# Grab the relevant $p$-value from the list
p_j=p_sort[j]
# Calculate the $p$-values of each of the order statistics (excluding the first gene)
out_p = append(out_p, pbeta(p_j,j,n+1-j) )
}
return(out_p)
}
order_stats_p_values_unscaled <- function(p_list){
# sort the list of $p$-values
p_sort <- sort(p_list)[2:length(p_list)]
#p_sort <- (p_sort_unscaled - min(p_list))/(1-min(p_list))
#p_sort <- (p_sort_unscaled - min(p_list))/(1-min(p_list))
n = length(p_sort)
out_p = c()
for (j in 1:length(p_sort)){
# Grab the relevant $p$-value from the list
p_j=p_sort[j]
# Calculate the $p$-values of each of the order statistics (excluding the first gene)
out_p = append(out_p, pbeta(p_j,j,n+1-j) )
}
return(out_p)
}
x<-function(){
## Let's try a Bayesian approach
# Bayes theorem:
# P(A|B) = P(B|A)P(A)/P(B)
# In our case, P(A|B) is what is the probability of the first order statistic given that
# we've screened on alpha_adap
#
# P(A) - the probability of the kth order stat
# P(B) - the probability of a gene being screened using alpha_adap
# P(B|A) - the probability that a gene was screened given that it was the kth order stat
# Here's a test case:
p_values <- c(0.001, 0.1, 0.12, 0.3, 0.4, 0.5, 0.8)
scaled_p_values <- scale_p_values(p_values)
# For the first order stat
alpha_adap <- 0.01
p_a <- pbeta(p_values[2],1,6+1-1)
p_b <- 1-alpha_adap
p_ba <- 1-pbeta(alpha_adap,1,6+1-1)
p_ab <- (p_ba*p_a)/p_b
pbeta(0.1,1,6+1-1)
}
generateNullData <- function(adaptation_screen, a, b, n){
temp <- c( rbeta(1,a,b),replicate(n-1, sample(10000,1)/10000) )
while (sum(temp<adaptation_screen)==0){
temp <- c( rbeta(1,a,b),replicate(n-1, sample(10000,1)/10000) )
}
return(temp)
}
generateAltData <- function(adaptation_screen, max, k, n){
if (n-k ==0){
temp <- c(replicate(k, sample(max,1)/10000))
}
else if(k==0){
temp<- c(replicate(n, sample(10000,1)/10000) )
}
else{
temp <- c(replicate(k, sample(max,1)/10000)
,c(replicate(n-k, sample(10000,1)/10000)) )
}
while (sum(temp<adaptation_screen)==0){
if (n-k ==0){
temp <- c(replicate(k, sample(max,1)/10000))
}
else if(k==0){
temp <- c(replicate(n, sample(10000,1)/10000) )
}
else{
temp <- c(replicate(k, sample(max,1)/10000)
,c(replicate(n-k, sample(10000,1)/10000)) )
}
}
return(temp)
}
runPowerAnalysis <- function(reps, alpha_1, n, power_vec, speciesVec){
emp_p_null_dat <- t(replicate(10000, generateNullData(alpha_1, 1, 1, n)))
emp_p_null_dat_unscaled <- t(apply(emp_p_null_dat ,1, order_stats_p_values_unscaled))
null_pMax_cor_unscaled <- cor( emp_p_null_dat_unscaled )
result_count= 0
output_DFs = list()
for (GEA_power in c(0.05, 1:9/10) ){
print(GEA_power)
topHits = 500/GEA_power
for (nSpecies in speciesVec){
print(nSpecies)
result_count = result_count +1
test_data <- t(replicate(reps,
generateAltData(alpha_1,
topHits,
nSpecies,
n)))
test_data <- t(apply(test_data, 1, sort))
test_data_order_stats_unscaled <- t(apply(test_data ,1, order_stats_p_values_unscaled))
test_data_order_stats_unscaled
binom_results = rep(-1, reps)
binom_adj_results = rep(-1, reps)
tippett_results = rep(-1, reps)
# ordmeta_all_results = rep(-1, reps)
# ordmeta_2plus_results = rep(-1, reps)
for (i in 1:reps){
print(i)
# print(test_data[i,])
unscaled_p_value_vector = test_data_order_stats_unscaled[i,]
temp_binom <- binom.test( sum(test_data[i,]<alpha_1), n, alpha_1, , alternative='greater')$p.value
temp_binom_adj <- binom.test( sum(test_data[i,]<alpha_1)-1, n-1, alpha_1, , alternative='greater')$p.value
temp_tippett <- tippett(unscaled_p_value_vector, adjust = "empirical", R = null_pMax_cor_unscaled, side = 1, size = 10000)$p
# temp_ordmeta_all <- ordmeta(test_data[i,])$p
# temp_ordmeta_2plus <- ordmeta(sort(test_data[i,])[2:length(test_data[i,])])$p
print(c(temp_tippett) )
binom_results[i] = temp_binom
binom_adj_results[i] = temp_binom_adj
tippett_results[i] = temp_tippett
# ordmeta_all_results[i] = temp_ordmeta_all
# ordmeta_2plus_results[i] = temp_ordmeta_2plus
}
output_DFs[[result_count]] = data.frame(GEA_Power=GEA_power,
nSpeciesConvergent=nSpecies,
power_1_binomial=sum(binom_results<power_vec[1])/reps,
power_1_binomial_adj=sum(binom_adj_results<power_vec[1])/reps,
power_1_tippett=sum(tippett_results<power_vec[1])/reps,
# power_1_ordmeta_all=sum(ordmeta_all_results<power_vec[1])/reps,
# power_1_ordmeta_2plus=sum(ordmeta_2plus_results<power_vec[1])/reps,
power_2_binomial=sum(binom_results<power_vec[2])/reps,
power_2_binomial_adj=sum(binom_adj_results<power_vec[2])/reps,
power_2_tippett=sum(tippett_results<power_vec[2])/reps)
# power_2_ordmeta_all=sum(ordmeta_all_results<power_vec[2])/reps,
# power_2_ordmeta_2plus=sum(ordmeta_2plus_results<power_vec[2])/reps)
}
}
output_temp <- do.call(rbind, output_DFs)
return(output_temp)
}
#################
### 7 species
Reps = 1000
Alpha_1 = 0.05
N = 7
Power_vec=c(0.01, 0.003757)
SpeciesVec = c(1:7)
#output_df_n7 <- runPowerAnalysis( Reps, Alpha_1, N, Power_vec, SpeciesVec )
#write.csv(output_df_n7,"~/UBC/GEA/pMax/Analysis/7_species_alpha_0.05_adap.csv")
output_df_n7 <- read.csv("~/UBC/GEA/pMax/Analysis/7_species_alpha_0.05_adap.csv")
#output_df_n7_alpha_1_01 <- runPowerAnalysis( Reps, 0.01, N, Power_vec, SpeciesVec )
#write.csv(output_df_n7_alpha_1_01,"~/UBC/GEA/pMax/Analysis/7_species_alpha_0.01_adap.csv")
output_df_n7_alpha_1_01 <- read.csv("~/UBC/GEA/pMax/Analysis/7_species_alpha_0.01_adap.csv")
#################
## N = 30
Reps_n30 = 1000
Alpha_1_n30 = 0.01
N_n30 = 30
Power_vec_n30=c(0.01, 0.003318)
SpeciesVec_n30 = c(1,2,5,10,20,30)
#output_df_n30 <- runPowerAnalysis( Reps_n30, Alpha_1_n30, N_n30, Power_vec_n30, SpeciesVec_n30 )
#write.csv(output_df_n30,"~/UBC/GEA/pMax/Analysis/30_species_alpha_0.01_adap.csv")
output_df_n30 <- read.csv("~/UBC/GEA/pMax/Analysis/30_species_alpha_0.01_adap.csv")
#output_df_n30$nSpeciesConvergent <- rep(c(1,2,5,10,20,30),10)
#################
## N = 30 - for sam
Reps_n30_s = 1000
Alpha_1_n30_s = 0.01
N_n30_s = 30
Power_vec_n30_s=c(0.03615, 0.003318)
SpeciesVec_n30_s = c(1,3,5,7,9,11)
#output_df_n30_s <- runPowerAnalysis( Reps_n30_s, Alpha_1_n30_s, N_n30_s, Power_vec_n30_s, SpeciesVec_n30_s )
#write.csv(output_df_n30_s,"~/UBC/GEA/pMax/Analysis/30_species_alpha_0.01_adap_forSam.csv")
output_df_n30_s <- read.csv("~/UBC/GEA/pMax/Analysis/30_species_alpha_0.01_adap_forSam.csv")
#################
## N = 3
Reps_n3 = 1000
Alpha_1_n3 = 0.05
N_n3 = 3
Power_vec_n3=c(0.0073)
SpeciesVec_n3 = c(0,1,2,3)
#output_df_n3 <- runPowerAnalysis( Reps_n3, Alpha_1_n3, N_n3, Power_vec_n3, SpeciesVec_n3 )
#write.csv(output_df_n3,"~/UBC/GEA/pMax/Analysis/3_species_alpha_0.05_adap.csv")
output_df_n3 <- read.csv("~/UBC/GEA/pMax/Analysis/3_species_alpha_0.05_adap.csv")
#################
## N = 2
Reps_n2 = 1000
Alpha_1_n2 = 0.05
N_n2 = 2
Power_vec_n2 = c(0.0073)
SpeciesVec_n2 = c(0,1,2)
output_df_n2 <- runPowerAnalysis( Reps_n2, Alpha_1_n2, N_n2, Power_vec_n2, SpeciesVec_n2 )
write.csv(output_df_n2,"~/UBC/GEA/pMax/Analysis/2_species_alpha_0.05_adap.csv")
output_df_n2 <- read.csv("~/UBC/GEA/pMax/Analysis/2_species_alpha_0.05_adap.csv")
###########
# Plot the data
#
# Start with n=7
# With Binomial Test
output_df_n7$nSpeciesConvergent <- factor(output_df_n7$nSpeciesConvergent,
levels = c(1,2,3,4,5,6,7),
labels = c("1/7",
"2/7",
"3/7",
"4/7",
"5/7",
"6/7",
"7/7"))
power_1_plot_n7 <- ggplot(data = output_df_n7)+
geom_hline(aes(yintercept = Power_vec[2]))+
geom_line(aes(x = GEA_Power,
y = power_2_tippett,
col = as.factor(nSpeciesConvergent),
lty = "PicMin"),
lwd=1)+
geom_line(aes(x = GEA_Power,
y = power_2_binomial,
col = as.factor(nSpeciesConvergent),
lty = "Binomial Test"),
lwd = 1)+
# ggtitle("Adaptation Outliers Identified\nUsing 95th Percentile")+
scale_y_continuous(expression("Probability of Rejecting Null Hypothesis ("*alpha[Repeated]*"=0.01)"),
limits = c(0,1))+
scale_x_continuous("Power of Genome Scan",
breaks = 1:9/10)+
scale_colour_manual("Number of\nLineages\nWhere the\nGene is Causal", values = colorBlindBlack8)+
scale_linetype_manual("Statistical Test", values = c(2,1))+
theme_bw()+
theme(
plot.title = element_text(hjust = 0.5)
)
### Now n=30 with binomial
output_df_n30$nSpeciesConvergent <- factor(output_df_n30$nSpeciesConvergent,
levels = c(1,2,5,10,20,30),
labels = c("1/30",
"2/30",
"5/30",
"10/30",
"20/30",
"30/30"))
power_1_plot_n30_adap_01 <- ggplot(data = output_df_n30)+
geom_hline(aes(yintercept = Power_vec_n30[2]))+
geom_line(aes(x = GEA_Power,
y = power_2_tippett,
col = as.factor(nSpeciesConvergent),
lty = "PicMin"),
lwd=1)+
geom_line(aes(x = GEA_Power,
y = power_2_binomial,
col = as.factor(nSpeciesConvergent),
lty = "Binomial Test"),
lwd = 1)+
# ggtitle("Adaptation Outliers Identified\nUsing 99th Percentile")+
scale_y_continuous(expression("Probability of Rejecting Null Hypothesis ("*alpha[Repeated]*"=0.00332)"),
limits = c(0,1))+
scale_x_continuous("Power of Genome Scan",
breaks = 1:9/10)+
scale_colour_manual("Number of\nLineages\nWhere the\nGene is Causal", values = colorBlindBlack8)+
scale_linetype_manual("Statistical Test", values = c(2,1))+
theme_bw()+
theme(
plot.title = element_text(hjust = 0.5)
)
# Supp Figure 5
S5 <- ggarrange(power_1_plot_n7,
power_1_plot_n30_adap_01,
ncol = 1,
nrow = 2,
labels = "AUTO")
png("~/UBC/GEA/pMax/writeUp/Plots/SuppFigure5.png",width = 500, height = 700)
print(S5)
dev.off()
# Same plot, but without the Binomial Test
F1_power_1_plot_n7 <- ggplot(data = output_df_n7_alpha_1_01)+
geom_hline(aes(yintercept = Power_vec[1]))+
geom_line(aes(x = GEA_Power,
y = power_2_tippett,
col = as.factor(nSpeciesConvergent)),
lwd=1)+
scale_y_continuous(expression("Power ("*alpha[Repeated]*"=0.01)"),
limits = c(0,1))+
scale_x_continuous("Power of Genome Scan",
breaks = 1:9/10)+
scale_colour_manual("Number of\nLineages\nWhere the\nGene is Causal", values = colorBlindBlack8)+
theme_bw()+
theme(
plot.title = element_text(hjust = 0.5)
)
### Now n=30 with binomial
F1_power_1_plot_n30_adap_01 <- ggplot(data = output_df_n30)+
geom_hline(aes(yintercept = Power_vec_n30[1]))+
geom_line(aes(x = GEA_Power,
y = power_2_tippett,
col = as.factor(nSpeciesConvergent)),
lwd=1)+
scale_y_continuous(expression("Power ("*alpha[Repeated]*"=0.01)"),
limits = c(0,1))+
scale_x_continuous("Power of Genome Scan",
breaks = 1:9/10)+
scale_colour_manual("Number of\nLineages\nWhere the\nGene is Causal", values = colorBlindBlack8)+
theme_bw()+
theme(
plot.title = element_text(hjust = 0.5)
)
# Figure 2
Figure2 <- ggarrange(F1_power_1_plot_n7,
F1_power_1_plot_n30_adap_01,
ncol = 1,
nrow = 2,
labels = "AUTO")
pdf("~/UBC/GEA/pMax/writeUp/Plots/Figure2.pdf",width = 5, height = 7)
print(Figure2)
dev.off()
###################
### Num Species Convergent
###
###
###
###
###
n = 7
alpha_1 = 0.05
emp_p_null_dat <- t(replicate(10000, generateNullData(alpha_1, 1, 1, n)))
emp_p_null_dat_unscaled <- t(apply(emp_p_null_dat ,1, order_stats_p_values_unscaled))
null_pMax_cor_unscaled <- cor( emp_p_null_dat_unscaled )
reps = 200
result_count= 1
output_DFs = list()
threshold= 0.05
for (GEA_power in c(0.2, 0.4, 0.6, 0.8) ){
topHits = 500/GEA_power
for (nSpecies in c(3,5,7)){
# for (nSpecies in c(3)){
for (i in 1:reps){
pass_test = FALSE
while (pass_test == FALSE){
test_data <- generateAltData(alpha_1,
topHits,
nSpecies,
n)
test_data_order_stats_unscaled <- order_stats_p_values_unscaled(test_data)
temp_binom <- binom.test( sum(test_data<alpha_1), n, alpha_1, , alternative='greater')$p.value
# temp_binom_adj <- binom.test( sum(test_data[i,]<alpha_1)-1, n-1, alpha_1, , alternative='greater')$p.value
temp_tippett <- tippett(test_data_order_stats_unscaled, adjust = "empirical", R = null_pMax_cor_unscaled, side = 1)$p
if ((temp_binom<threshold)&(temp_tippett<threshold)){
binom_count = sum(test_data<alpha_1)
PicMin_estimate = which.min( test_data_order_stats_unscaled )+1
print(test_data)
print(c(GEA_power, nSpecies, i, binom_count, PicMin_estimate))
print("'")
output_DFs[[result_count]] = data.frame(GEA_Power=GEA_power,
nSpeciesConvergent=nSpecies,
binomial_estimate=binom_count,
PicMin_estimate=PicMin_estimate)
result_count = result_count +1
pass_test = TRUE
}
}
}
}
}
#output_temp <- do.call(rbind, output_DFs)
#write.csv(output_temp, "~/UBC/GEA/pMax/Analysis/convergenceConfigurations_n7_a0.05.csv")
output_temp <- read.csv("~/UBC/GEA/pMax/Analysis/convergenceConfigurations_n7_a0.05.csv")
data <- melt(output_temp, id = c("GEA_Power", "nSpeciesConvergent", "X"))
data$variable <- factor( data$variable,
levels = c("binomial_estimate",
"PicMin_estimate"),
labels = c(expression("Simple Threshold ("*italic(ep)*"-value < 0.05)"),
expression(italic("PicMin")*" Estimate"))
)
data$GEA_Power <- paste("Genome Scan Power = ", data$GEA_Power, sep = "")
safe_colorblind_palette <- c("#88CCEE", "#CC6677", "#DDCC77", "#117733", "#332288", "#AA4499",
"#44AA99", "#999933", "#882255", "#661100", "#6699CC", "#888888")
colourPal <- safe_colorblind_palette[c(3,7,11)]
data$indicator <- data$value == data$nSpeciesConvergent
tbl <- with(data, table(nSpeciesConvergent, variable, GEA_Power, value))
plot_data <- as.data.frame(tbl)
plot_data$indicator <- as.numeric(as.character(plot_data$value)) == as.numeric(as.character(plot_data$nSpeciesConvergent))
plot_data$nSpeciesConvergent_lab <- paste(plot_data$nSpeciesConvergent, " Lineages With Causal Gene", sep = "")
parse.labels <- function(x) parse(text = x)
config_plot <- ggplot(plot_data, aes(value, Freq, fill = variable, col = indicator)) +
geom_col(position = 'dodge')+
scale_color_manual(values = c("white","black"))+
scale_x_discrete("Estimate of the Number of Lineages Where the Gene is Causal",
breaks = c(1,2,3,4,5,6,7)) +
scale_y_continuous(limits = c(0,200))+
#coord_cartesian(clip = "off") +
scale_fill_manual("", values = colourPal, labels = parse.labels)+
guides(alpha = "none", colour = "none")+
facet_grid(nSpeciesConvergent_lab~GEA_Power)+
theme_bw()+
theme(
axis.title.y = element_blank() ,
axis.title.x = element_text( hjust = 0.5),
strip.background = element_blank(),
strip.text.y = element_text(vjust = 1),
legend.position = "bottom"
)
ggsave("~/UBC/GEA/pMax/writeUp/Plots/Figure3.pdf",config_plot,
width = 8,
height = 7)
ggsave("~/UBC/GEA/pMax/writeUp/Plots/Figure3.png", config_plot,
device = png,
units = "in",
width = 8, height = 7,
res = 1000)
###########
#### Config plot n=30
n = 30
alpha_1 = 0.01
emp_p_null_dat <- t(replicate(10000, generateNullData(alpha_1, 1, 1, n)))
emp_p_null_dat_unscaled <- t(apply(emp_p_null_dat ,1, order_stats_p_values_unscaled))
null_pMax_cor_unscaled <- cor( emp_p_null_dat_unscaled )
reps = 200
result_count= 1
output_DFs = list()
threshold= 0.045
for (GEA_power in c(0.2, 0.4, 0.6, 0.8) ){
topHits = 500/GEA_power
for (nSpecies in c(3,5,7,9,11)){
# for (nSpecies in c(3)){
for (i in 1:reps){
pass_test = FALSE
while (pass_test == FALSE){
test_data <- generateAltData(alpha_1,
topHits,
nSpecies,
n)
test_data_order_stats_unscaled <- order_stats_p_values_unscaled(test_data)
temp_binom <- binom.test( sum(test_data<alpha_1), n, alpha_1, , alternative='greater')$p.value
# temp_binom_adj <- binom.test( sum(test_data[i,]<alpha_1)-1, n-1, alpha_1, , alternative='greater')$p.value
temp_tippett <- tippett(test_data_order_stats_unscaled, adjust = "empirical", R = null_pMax_cor_unscaled, side = 1)$p
if ((temp_binom<threshold)&(temp_tippett<threshold)){
binom_count = sum(test_data<alpha_1)
PicMin_estimate = which.min( test_data_order_stats_unscaled )+1
print(test_data)
print(c(GEA_power, nSpecies, i, binom_count, PicMin_estimate))
print("'")
output_DFs[[result_count]] = data.frame(GEA_Power=GEA_power,
nSpeciesConvergent=nSpecies,
binomial_estimate=binom_count,
PicMin_estimate=PicMin_estimate)
result_count = result_count +1
pass_test = TRUE
}
}
}
}
}
output_temp <- do.call(rbind, output_DFs)
data <- melt(output_temp, id = c("GEA_Power", "nSpeciesConvergent"))
data$variable <- factor( data$variable,
levels = c("binomial_estimate",
"PicMin_estimate"),
labels = c(expression(atop("Simple Threshold", paste("("*italic(ep)*"-value <0.05)"))),
expression(atop(italic("PicMin"), paste("Estimate")))
)
)
data$GEA_Power <- paste("Genome Scan Power = ", data$GEA_Power, sep = "")
expression(atop("Simple Threshold", paste("("*italic(ep)*"-value <0.05)")))
safe_colorblind_palette <- c("#88CCEE", "#CC6677", "#DDCC77", "#117733", "#332288", "#AA4499",
"#44AA99", "#999933", "#882255", "#661100", "#6699CC", "#888888")
colourPal <- safe_colorblind_palette[c(3,7,11)]
data$indicator <- data$value == data$nSpeciesConvergent
tbl <- with(data, table(nSpeciesConvergent, variable, GEA_Power, value))
plot_data <- as.data.frame(tbl)
plot_data$indicator <- as.numeric(as.character(plot_data$value)) == as.numeric(as.character(plot_data$nSpeciesConvergent))
plot_data$nSpeciesConvergent_lab <- paste(plot_data$nSpeciesConvergent, " Species Using Gene", sep = "")
plot_data$nSpeciesConvergent_lab <- factor(plot_data$nSpeciesConvergent_lab,
levels = c("3 Species Using Gene",
"5 Species Using Gene",
"7 Species Using Gene",
"9 Species Using Gene",
"11 Species Using Gene"))
config_plot <- ggplot(plot_data, aes(value, Freq, fill = variable)) +
geom_col(position = 'dodge')+
geom_col(data= plot_data[plot_data$indicator == TRUE,], aes(fill = variable),position = 'dodge',col ="black")+
scale_x_discrete("Estimate of the Number of Lineages Using the Gene",
breaks = c(5,10,15,20,25,30)) +
scale_y_continuous(limits = c(0,200))+
#coord_cartesian(clip = "off") +
scale_fill_manual("", values = colourPal)+
guides(alpha = "none", colour = "none")+
facet_grid(nSpeciesConvergent_lab~GEA_Power)+
theme_bw()+
theme(
axis.title.y = element_blank() ,
axis.title.x = element_text( hjust = 0.5),
strip.background = element_blank(),
strip.text.y = element_text(vjust = 1),
legend.position = "bottom"
)
ggsave("~/UBC/GEA/pMax/Analysis/convergenceConfigurations_n30_a0.05_t0.045_s.pdf",config_plot,
width = 14,
height = 10)
######
#
# Full genome comparison
#
#
nGenes <- 10000
nSpecies <- 7
nHits <- 100
# First, simulate p-values for each species
speciesEmpiricalP <- function(nGenes, nHits, a, b){
species_p_values <- c( rbeta( nHits, a,b),
runif(nGenes-nHits) )
species_empirical_p_values <- rank(species_p_values)/nGenes
return(species_empirical_p_values)
}
sum(rbeta(1000, 0.2,5)<0.05)
GEA_power_list <- list(
list(a=0.8, b = 5, power=0.3), # ~30% power
# list(a=0.66, b = 5, power=0.4), # ~40% power
list(a=0.5, b = 5, power=0.5), # ~50% power
# list(a=0.4, b = 5, power=0.6), # ~60% power
list(a=0.3, b = 5, power = 0.7), # ~70% power
# list(a=0.2, b = 5, power = 0.8), # ~70% power
list(a=0.1, b = 5, power = 0.9) # ~90% power
)
alpha_a <- 0.05
# Run 10,000 replicate simulations of this situation and build the correlation matrix
emp_p_null_dat <- t(replicate(40000, generateNullData(alpha_a, 0.5, 3, 7)))
# Calculate the order statistics p-values for each simulation
emp_p_null_dat_unscaled <- t(apply(emp_p_null_dat ,1, order_stats_p_values_unscaled))
# Use those p-values to construct the correlation matrix
null_pMax_cor_unscaled <- cor( emp_p_null_dat_unscaled )
output_DF_list <- list()
result_count=0
for (k in c(3,5,7)){
# for (k in c(7)){
for (g in GEA_power_list){
for (rep in 1:10){
print(c(k,g$power,rep))
result_count = result_count+1
if (k < 7){
dataSet <- cbind( c(replicate(k, speciesEmpiricalP(nGenes, nHits, g$a, g$b)) ),
c(replicate(7-k, speciesEmpiricalP(nGenes, 0, 1, 1) ) ))
}
else if (k == 7){
dataSet <- cbind( replicate(k, speciesEmpiricalP(nGenes, nHits, g$a, g$b)) )
}
adap_screen_vector <- apply(dataSet<0.05, 1, sum)!=0
gene_names <- c(1:nGenes)[adap_screen_vector]
screenedDataSet <- dataSet[adap_screen_vector, ]
registerDoParallel(numCores)
output_p_values<- foreach (i=1:nrow(screenedDataSet), .combine=c) %dopar% {
# output_p_values<- foreach (i=1:100, .combine=c) %dopar% {
PicMin(screenedDataSet[i,], null_pMax_cor_unscaled)$p
}
q_values <- p.adjust( output_p_values, method = "fdr" )
temp_df <- as.data.frame(screenedDataSet)
temp_df$gene_id = gene_names
temp_df$q <- q_values
true_positives <- sum((temp_df$q<0.05)&(temp_df$gene_id<=nHits))
false_positives <- sum((temp_df$q<0.05)&(temp_df$gene_id>nHits))
output_DF_list[[result_count]] = data.frame(GEA_Power=g$power,
rep = rep,
nSpeciesConvergent=k,
true_positives=true_positives,
false_positives=false_positives)
}
}
}
temp <- do.call(rbind, output_DF_list)
#write.csv(temp, "~/UBC/GEA/pMax/Analysis/genome_analysis_3-7.csv")
temp <- read.csv("~/UBC/GEA/pMax/Analysis/genome_analysis_3-7.csv")
temp$GEA_Power <- as.character(temp$GEA_Power)
temp$nSpeciesConvergent <- as.character(temp$nSpeciesConvergent)
temp_melt <- melt(temp,
id = c("GEA_Power", "rep", "nSpeciesConvergent", "X"))
temp_melt$variable <- factor(temp_melt$variable,
levels = c("true_positives","false_positives"),
labels = c("True Positives", "False Positives"))
genome_fdr_plot <- ggplot(data = temp_melt, aes(x = as.factor(GEA_Power), y = value, fill = nSpeciesConvergent,col = nSpeciesConvergent))+
geom_boxplot(alpha = 0.5)+
scale_y_continuous("Number of Genes Identified\n(q<0.05)",
limits = c(0,100))+
scale_x_discrete("Genome Scan Power")+
facet_wrap(~variable)+
scale_fill_manual("Number of\nLineages\nWhere the\nGene is Causal"
,values = colorBlindBlack8[c(3,5,7)])+
scale_color_manual("Number of\nLineages\nWhere the\nGene is Causal"
,values = colorBlindBlack8[c(3,5,7)])+
theme_bw()+
theme(
strip.background = element_blank(),
strip.text = element_text(size = 12)
)
ggsave("~/UBC/GEA/pMax/writeUp/Plots/Plot4.pdf",
genome_fdr_plot,
width = 8,
height = 4)
ggsave("~/UBC/GEA/pMax/writeUp/Plots/Plot4.png",
genome_fdr_plot,
units = "in",
device = "png",
width = 8,
height = 4)
############
### Alpha_a threshold
###
runPowerAnalysis_alpha1 <- function(reps, GEA_power_vec, n, power_vec, speciesVec){
output_DFs = list()
result_count= 0
for (alpha_1 in c(10,50,100,500,1000)/10000 ){
emp_p_null_dat <- t(replicate(10000, generateNullData(alpha_1, 1, 1, n)))
emp_p_null_dat_unscaled <- t(apply(emp_p_null_dat ,1, order_stats_p_values_unscaled))
null_pMax_cor_unscaled <- cor( emp_p_null_dat_unscaled )
for (GEA_power in GEA_power_vec){
print(GEA_power)
topHits = 500/GEA_power
for (nSpecies in speciesVec){
print(nSpecies)
result_count = result_count +1
test_data <- t(replicate(reps,
generateAltData(alpha_1,
topHits,
nSpecies,
n)))
test_data <- t(apply(test_data, 1, sort))
test_data_order_stats_unscaled <- t(apply(test_data ,1, order_stats_p_values_unscaled))
test_data_order_stats_unscaled
# binom_results = rep(-1, reps)
# binom_adj_results = rep(-1, reps)
tippett_results = rep(-1, reps)
# ordmeta_all_results = rep(-1, reps)
# ordmeta_2plus_results = rep(-1, reps)
for (i in 1:reps){
print(i)
# print(test_data[i,])
unscaled_p_value_vector = test_data_order_stats_unscaled[i,]
# temp_binom <- binom.test( sum(test_data[i,]<alpha_1), n, alpha_1, , alternative='greater')$p.value
# temp_binom_adj <- binom.test( sum(test_data[i,]<alpha_1)-1, n-1, alpha_1, , alternative='greater')$p.value
temp_tippett <- tippett(unscaled_p_value_vector, adjust = "empirical", R = null_pMax_cor_unscaled, side = 1, size = 10000)$p
# temp_ordmeta_all <- ordmeta(test_data[i,])$p
# temp_ordmeta_2plus <- ordmeta(sort(test_data[i,])[2:length(test_data[i,])])$p
print(c(temp_tippett) )
# binom_results[i] = temp_binom
# binom_adj_results[i] = temp_binom_adj
tippett_results[i] = temp_tippett
# ordmeta_all_results[i] = temp_ordmeta_all
# ordmeta_2plus_results[i] = temp_ordmeta_2plus
}
output_DFs[[result_count]] = data.frame(alpha_a=alpha_1,
nSpeciesConvergent=nSpecies,
GEA_Power=GEA_power,
# power_1_binomial=sum(binom_results<power_vec[1])/reps,
# power_1_binomial_adj=sum(binom_adj_results<power_vec[1])/reps,
power_1_tippett=sum(tippett_results<power_vec[1])/reps,
# power_1_ordmeta_all=sum(ordmeta_all_results<power_vec[1])/reps,
# power_1_ordmeta_2plus=sum(ordmeta_2plus_results<power_vec[1])/reps,
# power_2_binomial=sum(binom_results<power_vec[2])/reps,
# power_2_binomial_adj=sum(binom_adj_results<power_vec[2])/reps,
power_2_tippett=sum(tippett_results<power_vec[2])/reps)
# power_2_ordmeta_all=sum(ordmeta_all_results<power_vec[2])/reps,
# power_2_ordmeta_2plus=sum(ordmeta_2plus_results<power_vec[2])/reps)
print( output_DFs[[result_count]])
}
}
}
output_temp <- do.call(rbind, output_DFs)
return(output_temp)
}
#######
### 7 species
Reps = 1000
GEA_power_vec = c(0.2,0.50, 0.7, 0.9)
N = 7
Power_vec=c(0.05, 0.01)
SpeciesVec = c(1:7)
#output_df_n7_alpha_screen <- runPowerAnalysis_alpha1( Reps, GEA_power_vec, N, Power_vec, SpeciesVec )
output_df_n7_alpha_screen <- read.csv("~/UBC/GEA/pMax/Analysis/falsePositives_alphaAdapt_n7.csv")
#write.csv(output_df_n7_alpha_screen,
# "~/UBC/GEA/pMax/Analysis/falsePositives_alphaAdapt_n7.csv")
single_hit <- output_df_n7_alpha_screen[output_df_n7_alpha_screen$nSpeciesConvergent==1,]
single_hit_melt <- melt(single_hit, id = c("alpha_a","nSpeciesConvergent","GEA_Power", "X"))
#single_hit_melt <- melt(single_hit, id = c("alpha_a","nSpeciesConvergent","X"))
## Calculate Clopper-Pearson intervals
single_hit_melt$lower_p <-qbeta(0.05/2,
single_hit_melt$value*Reps,
Reps - single_hit_melt$value*Reps +1)
single_hit_melt$upper_p <-qbeta(1-0.05/2,
single_hit_melt$value*Reps+1,
Reps - single_hit_melt$value*Reps)
single_hit_melt$variable <- factor(single_hit_melt$variable,
levels = c("power_1_tippett","power_2_tippett"),
labels = c(expression(alpha[Repeated]*" = 0.05"),
expression(alpha[Repeated]*" = 0.01")))
lineref_df <- data.frame(value = c(0.05,0.01), variable = c("power_1_tippett","power_2_tippett"))
lineref_df$variable <- factor(lineref_df$variable,
levels = c("power_1_tippett","power_2_tippett"),
labels = c(expression(alpha[Repeated]*" = 0.05"),
expression(alpha[Repeated]*" = 0.01")))
##############
### 30 species
###
Reps = 1000
GEA_power_vec = c(0.2, 0.50, 0.7, 0.9)
N = 30
Power_vec=c(0.05, 0.01)
SpeciesVec = c(1)
#output_df_n30_alpha_screen <- runPowerAnalysis_alpha1( Reps, GEA_power_vec, N, Power_vec, SpeciesVec )
output_df_n30_alpha_screen <- read.csv("~/UBC/GEA/pMax/Analysis/falsePositives_alphaAdapt_n30.csv")
#write.csv(output_df_n30_alpha_screen,
# "~/UBC/GEA/pMax/Analysis/falsePositives_alphaAdapt_n30.csv")
single_hit_n30 <- output_df_n30_alpha_screen[output_df_n30_alpha_screen$nSpeciesConvergent==1,]
single_hit_n30_melt <- melt(single_hit_n30, id = c("alpha_a","nSpeciesConvergent","GEA_Power", "X"))
#single_hit_melt <- melt(single_hit, id = c("alpha_a","nSpeciesConvergent","X"))
## Calculate Clopper-Pearson intervals
single_hit_n30_melt$lower_p <-qbeta(0.05/2,
single_hit_n30_melt$value*Reps,
Reps - single_hit_n30_melt$value*Reps +1)
single_hit_n30_melt$upper_p <-qbeta(1-0.05/2,
single_hit_n30_melt$value*Reps+1,
Reps - single_hit_n30_melt$value*Reps)
single_hit_n30_melt$variable <- factor(single_hit_n30_melt$variable,
levels = c("power_1_tippett","power_2_tippett"),
labels = c(expression(alpha[Repeated]*" = 0.05"),
expression(alpha[Repeated]*" = 0.01")))
single_hit_n30_melt$n <- "30"
single_hit_melt$n <- "7"
plot_single_hit_data <- rbind(single_hit_melt,single_hit_n30_melt)
plot_single_hit_data$n <- factor( plot_single_hit_data$n ,
levels = c("7","30"),
labels = c(expression(7*" Species"),
expression(30*" Species")))
alpha_adapt_FP_plot <- ggplot(data = plot_single_hit_data)+
geom_point(aes( x= as.factor(GEA_Power),
y= value,
col = as.factor(alpha_a)),
position=position_dodge(width=0.25))+
geom_errorbar(aes( x= as.factor(GEA_Power),
y= value,
col = as.factor(alpha_a),
ymin = lower_p,
ymax = upper_p),
position=position_dodge(width=0.25),
width = 0.01)+
scale_x_discrete("Genome Scan Power")+
scale_y_continuous("Proportion of False Positives",
limits =c(0,0.15))+
geom_hline(data = lineref_df, aes(yintercept = value), lty = 2)+
facet_grid(n~variable,
scale = "free_y",
label=label_parsed)+
scale_color_brewer(expression(alpha[Adapt]),
palette="Dark2")+
theme_bw()+
theme(
strip.background = element_blank(),
strip.text.x = element_text(size = 12)
)
ggsave("~/UBC/GEA/pMax/Analysis/falsePositives_alphaAdapt.pdf", alpha_adapt_FP_plot,
width = 7, height = 5)
ggsave("~/UBC/GEA/pMax/Analysis/falsePositives_alphaAdapt.png", device = png,
alpha_adapt_FP_plot,
units = "in",
width = 7, height = 5,
res = 1000)
multi_n_data <- output_df_n7_alpha_screen[output_df_n7_alpha_screen$nSpeciesConvergent>1,]
multi_n_data_melt <- melt(multi_n_data,
id = c("X","alpha_a","nSpeciesConvergent","GEA_Power"))
label_vec <- c(expression(2*" Species Convergent"),
expression(3*" Species Convergent"),
expression(4*" Species Convergent"),
expression(5*" Species Convergent"),
expression(6*" Species Convergent"),
expression(7*" Species Convergent"))
multi_n_data_melt$variable <- factor(multi_n_data_melt$variable,
levels = c("power_1_tippett","power_2_tippett"),
labels = c("alpha[Repeated]*' = 0.05'",
"alpha[Repeated]*' = 0.01'"))
multi_n_data_melt$n <- factor(multi_n_data_melt$nSpeciesConvergent,
levels = 2:7,
labels = label_vec)
#paste(multi_n_data_melt$nSpeciesConvergent, "Species Convergent")
alpha_adapt_TP_plot <- ggplot(data = multi_n_data_melt)+
geom_line(aes( x= as.factor(GEA_Power),
y= value,
col = as.factor(alpha_a),
group = as.factor(alpha_a)),
lwd= 1)+
facet_grid(n~variable,
labeller = label_parsed)+
scale_x_discrete("Genome Scan Power")+
scale_y_continuous("Probability of Rejecting Null Hypothesis")+
scale_color_brewer(expression(alpha[Adapt]),
palette="Dark2")+
theme_bw()+
theme(
strip.background = element_blank(),
strip.text.x = element_text(size = 12)
)
ggsave("~/UBC/GEA/pMax/Analysis/truePositives_alphaAdapt.pdf", alpha_adapt_TP_plot,
width = 7, height = 12)
ggsave("~/UBC/GEA/pMax/Analysis/truePositives_alphaAdapt.png", alpha_adapt_TP_plot,
device = png,
units = "in",
width = 7, height = 12,
res = 1000)
###################
# p-value distributions under the null
#' @title Generate data under the null hypothesis
#' @param adaptation_screen The threshold used to determine adaptation
#' @param a the 'a' parameter of a beta distribution of p-values for the false null
#' @param b the 'b' parameter of a beta distribution of p-values for the false null
#' @param n the number of species in the test
#' @param genes the number of genes in the genome use to calculate empirical p-values
#' @importFrom "stats" "rbeta"
GenerateNullData <- function(adaptation_screen, a, b, n, genes){
temp <- c( rbeta(1,a,b),replicate(n-1, sample(genes,1)/genes) )
while (sum(temp<adaptation_screen)==0){
temp <- c( rbeta(1,a,b),replicate(n-1, sample(genes,1)/genes) )
}
return(temp)
}
replicates = 100
n=7
results_DFs <- list()
count = 0
for (alpha_adapt in c(0.005, 0.01, 0.05, 1.0)){
null_data_for_mat <- t(replicate(40000, GenerateNullData(alpha_adapt, 0.5, 3, n, 10000)))
emp_p_null_dat_unscaled <- t(apply(null_data_for_mat,
1,
order_stats_p_values_unscaled))
# Use those p-values to construct the correlation matrix
null_pMax_cor_unscaled <- cor( emp_p_null_dat_unscaled )
for (maxP in c(600, 1000, 2000)){
count = count + 1
print(c(alpha_adapt, maxP))
null_data <- t( replicate( replicates,
generateAltData(alpha_adapt,
maxP,
1,
n) ) )
null_data_order_stats <- t(apply(null_data_for_mat,
1,
order_stats_p_values_unscaled))
result_vector = rep(0,
replicates)
print("!")
for (s in seq_len(replicates)){
print(s)
temp_tippett <- tippett(null_data_order_stats[s,],
adjust = "empirical",
R = null_pMax_cor_unscaled,
side = 1,
size = 10000)$p
result_vector[s] = temp_tippett
}
results_DFs[[count]] = data.frame(p_vals = result_vector,
alpha_adapt = rep(alpha_adapt, replicates),
maxP = rep(maxP, replicates))
}
}
histo_data <- do.call( rbind, results_DFs )
histo_data$alpha_adapt <- factor(histo_data$alpha_adapt,
levels = c(0.005,0.01,0.05,1.0),
labels = c(expression(alpha[Adapt]*"=0.005"),
expression(alpha[Adapt]*"=0.01"),
expression(alpha[Adapt]*"=0.05"),
expression(alpha[Adapt]*"=1.0")))
histo_data$maxP <- 1/(histo_data$maxP/500)
histo_data$scan_power <- factor(histo_data$maxP,
levels = c(5/6, 0.5, 0.25),
labels = c(expression("Genome scan power"*" = 0.8333..."),
expression("Genome scan power"*" = 0.5"),
expression("Genome scan power"*" = 0.25")))
null_hypothesis_histogram <- ggplot(data = histo_data,
aes(x = p_vals))+
geom_histogram( binwidth = 0.05, boundary = 1, fill = "grey", col = "black" )+
facet_grid(scan_power~alpha_adapt,
label = label_parsed)+
scale_x_continuous(expression(italic("p")*"-value"))+
# scale_y_continuous("Count",
# limits = c(0,700))+
theme_bw()+
theme(
strip.text.x = element_text(size = 13),
strip.background = element_blank()
)
ggsave("~/UBC/GEA/pMax/Analysis/null_distribution_histogram_n7.pdf",
width = 9.50,
height = 6.50,
units = "in")
replicates = 10000
n=30
results_DFs <- list()
count = 0
for (alpha_adapt in c(0.005, 0.01, 0.05, 1.0)){
for (maxP in c(600, 1000, 2000)){
count = count + 1
null_data_for_mat <- t(replicate(40000, GenerateNullData(alpha_adapt, 0.5, 3, n, 10000)))
emp_p_null_dat_unscaled <- t(apply(null_data_for_mat,
1,
order_stats_p_values_unscaled))
# Use those p-values to construct the correlation matrix
null_pMax_cor_unscaled <- cor( emp_p_null_dat_unscaled )
null_data <- t( replicate( replicates,
generateAltData(alpha_adapt,
maxP,
1,
n) ) )
null_data_order_stats <- t(apply(null_data_for_mat,
1,
order_stats_p_values_unscaled))
result_vector = rep(0,
replicates)
for (s in seq_len(replicates)){
temp_tippett <- tippett(null_data_order_stats[s,],
adjust = "empirical",
R = null_pMax_cor_unscaled,
side = 1,
size = 10000)$p
result_vector[s] = temp_tippett
}
results_DFs[[count]] = data.frame(p_vals = result_vector,
alpha_adapt = rep(alpha_adapt, replicates),
maxP = rep(maxP, replicates))
}
}
histo_data <- do.call( rbind, results_DFs )
histo_data$alpha_adapt <- factor(histo_data$alpha_adapt,
levels = c(0.005,0.01,0.05,1.0),
labels = c(expression(alpha[Adapt]*"=0.005"),
expression(alpha[Adapt]*"=0.01"),
expression(alpha[Adapt]*"=0.05"),
expression(alpha[Adapt]*"=1.0")))
histo_data$maxP <- 1/(histo_data$maxP/500)
histo_data$scan_power <- factor(histo_data$maxP,
levels = c(5/6, 0.5, 0.25),
labels = c(expression("Genome scan power"*" = 0.8333..."),
expression("Genome scan power"*" = 0.5"),
expression("Genome scan power"*" = 0.25")))
null_hypothesis_histogram <- ggplot(data = histo_data,
aes(x = p_vals))+
geom_histogram( binwidth = 0.05, boundary = 1, fill = "grey", col = "black" )+
facet_grid(scan_power~alpha_adapt,
label = label_parsed)+
scale_x_continuous(expression(italic("p")*"-value"))+
scale_y_continuous("Count",
limits = c(0,700))+
theme_bw()+
theme(
strip.text.x = element_text(size = 13),
strip.background = element_blank()
)
ggsave("~/UBC/GEA/pMax/Analysis/null_distribution_histogram_n30.pdf",
width = 9.50,
height = 6.50,
units = "in")
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